Software load balancer to maximize utilization

ABSTRACT

A load balancer receives a sequence of requests for computing service and distributes the requests for computing service to a computing node in an ordered list of computing nodes until the computing node reaches its maximum allowable compute capability. Responsive to an indication that the computing node has reached its maximum allowable compute capability, the load balancer distributes subsequent requests for computing service to another computing node in the ordered list. If the computing node is the last computing node in the ordered list, the load balancer distributes a subsequent request for computing service to a computing node other than one of the computing nodes in the ordered list of computing nodes. If the computing node is not the last computing node in the ordered list, the load balancer distributes a subsequent request for computing service to another computing node in the ordered list of computing nodes.

CLAIM OF PRIORITY

This application is a continuation-in-part of, and claims the benefitof, U.S. patent application Ser. No. 14/586,814, filed Dec. 30, 2014,which claims benefit of U.S. Provisional Patent Application Ser. No.61/984,603, filed on Apr. 25, 2014. The contents of both applicationsare incorporated by reference herein in their entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the processingof data. Specifically, the present disclosure addresses systems andmethods to facilitate load balancing in the efficient use of resourcesto ensure adherence to SLA response times.

BACKGROUND

Large scale computing systems process work by distributing the incomingwork across a set of machines running similar software. What is desiredis a load balancer which may be an efficient software load balancer thatwill distribute the work across a minimum set of machines and reduceoverall cost of performing the given work at any given time.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a network diagram illustrating a network environment suitablefor performing data processing, according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a server machinesuitable for work load balancing in a network environment, according tosome example embodiments.

FIG. 3 is a block diagram illustrating components of a device suitablefor a workload balancer, according to some example embodiments.

FIG. 4 is a flowchart illustrating data flows within the networkenvironment suitable for load balancing, according to some exampleembodiments.

FIG. 4A is a further flowchart illustrating data flows within thenetwork environment suitable for load balancing, according to someexample embodiments.

FIG. 5 is a block diagram illustrating components of a device suitablefor a workload balancer with a plurality of group load balancer modules,according to some example embodiments.

FIG. 6 is a flowchart illustrating a method of routing requests,according to some example embodiments.

FIG. 7 is a flowchart illustrating a method of routing requests in adegraded state, according to some example embodiments.

FIG. 8 is a flowchart illustrating a method of updating a maximum numberof connections for a node, according to some example embodiments.

FIG. 9 is a flowchart illustrating a method of updating a maximum numberof connections for a node in a degraded state, according to some exampleembodiments.

FIG. 10A is a graph illustrating how many transactions are beingprocessed by each active machine, or node, of an ordered list of nodes,at various load levels as illustrated in FIG. 11, according to someembodiments.

FIG. 10B is a graph illustrating a utilization level of the activemachines or nodes, of an ordered list of nodes, at various load levelsas illustrated in FIG. 11, according to some embodiments.

FIG. 10C is a graph illustrating the average response time of thetransactions processed by active machines, or nodes, of an ordered listof nodes, at various load levels as illustrated in FIG. 11, according tosome embodiments.

FIG. 11 is a graph illustrating steadily increasing, and thendecreasing, transactions arriving for an ordered list of nodes,according to some embodiments.

FIG. 12 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

Example methods and systems are directed to software load balancers.Examples merely typify possible variations. Unless explicitly statedotherwise, components and functions are optional and may be combined orsubdivided, and operations may vary in sequence or be combined orsubdivided. In the following description, for purposes of explanation,numerous specific details are set forth to provide a thoroughunderstanding of example embodiments. It will be evident to one skilledin the art, however, that the present subject matter may be practicedwithout these specific details.

Traditional load balancers use a given set of target machines to sendthe load to as evenly as possible among the members of the set.Different load balancing methods achieve this either by using a staticload balancing method or by using some information on the state of thetarget machines. But this type of load balancer is not necessarilydistributing the work evenly or efficiently using resources. If thereare slower machines, the load balancer might give less work to theslower machines and more work to the faster machines. However, this canresult in problems because of the possibility of multiple machinetechnologies being in the same group. Stated another way, there can beslower machines and faster machines in the set and the system will notbe able to automatically distribute the load based on the need of thejobs at hand and the availability of resources or machines in the set orthe capability of resources or machines in the set. This usually resultsin the work not being balanced among the machines in a set, with somemachines operating on a large number of jobs while other, perhapsslower, machines, operating on a smaller number of jobs, yet most of themachines being engaged at all times irrespective of available work. Inall cases, the total number of machines operational will be the entireset. Consequently, both power and resources are used inefficiently.Stated another way, the traditional load balancer is static, much likean appliance. The load balancer statically operates without a degree offreedom that would allow it to listen to the detailed feedback of thesystem being balanced, and react accordingly. Consequently, thetraditional load balancer does not discard unnecessary capacity nor doesit add capacity as needed; it uses all machines in a pool at all times.

Example embodiments provide a load balancer which distributes workacross each machine in a set of machines (which may be virtual machines)until each machine reaches its maximum allowable compute capabilitybefore distributing work to the next machine in the set, withoutimpacting the user experience. This type of load balancer avoids theinefficiency problem caused by slower and faster machines in the set.For example, if the system comprises a set of ten machines, the numberof machines needed to perform the work at hand at a given time mightjust be three. The load balancer distributes the work to each machineuntil that machine reaches its maximum, and then begins distributingwork to the next machine, and so on. The result, in this example, wouldbe that three machines would be used for the work. The other seven couldbe used for other system work or could be powered down as desired inorder to conserve power. This is achieved by a dynamic software loadbalancer in which the usual service level agreement (SLA) determines anexpected response time, and feedback from the machines determines, orenables the load balancer to determine, how many jobs are pushed to theminimum number of machines in the set of machines to do the work.

Every machine that is load balanced in the set may have local managementsoftware (sometimes called a management system) that is used to manageapplication software on that machine. When a new version of anapplication is deployed, the management system brings the localmanagement software up and then may start a load balancer agent, if anagent is present. The agent then communicates with the load balancer forregistering itself as ready to receive traffic. The overall system alsoprovides that a node, (e.g., machine) trying to register is valid andpart of a pool that is authorized to receive work. After this, the nodestarts receiving work from the load balancer when it is needed. The loadbalancer will continue sending traffic to the node until it reachesresource saturation, or until an SLA can be met, at that point the loadbalancer does not send any new work until the node has finished withsome of the work it already has. When there is not enough work to giveto this node, it is released from the load balancer and can bereclaimed, or can remain idle until it is needed again. In practice themachines can function with or without the agent.

The load balancer will look at all the machines registered for a givenfunction and will start traffic to those machines, using some order itdetermines randomly. As the load balancer sends traffic to a machine, itwill receive feedback from that machine that tells the load balancer howmuch more work the machine can accept that can be completed within thespecified SLA. The load balancer continues to send traffic to thatmachine until it is saturated. Once that occurs, the load balancerstarts sending traffic to the next machine in the group and so on. Whenthe incoming traffic starts slowing down, the machine with least work inprogress is idled and potentially released if the load balancer does nothave enough traffic to send.

An overall management control plan may look at the entire systemholistically and decide whether it needs to add more capacity to a givenpool. The decision may be based on traffic patterns and system behavior.If the amount of work goes down below a threshold and a lot of machinesbecome idle, the management control plan can reclaim these idle machinesand use them for other work or simply turn them off to conserve power.

FIG. 1 is a network diagram illustrating a network environment 100suitable for software load balancers, according to some exampleembodiments. The network environment 100 includes a server machine 110,a database 115 connected to server machine 110, and devices 130 and 150,all communicatively coupled to each other via a network 190. The servermachine 110 may form all or part of a network-based system 105 (e.g., acloud-based server system configured to provide one or more services tothe devices 130 and 150). The server machine 110 and the devices 130 and150 may each be implemented in a computer system, in whole or in part,as described below with respect to FIG. 12.

Also shown in FIG. 1 are users 132 and 152. One or both of the users 132and 152 may be a human user (e.g., a human being), a machine user (e.g.,a computer configured by a software program to interact with the device130), or any suitable combination thereof (e.g., a human assisted by amachine or a machine supervised by a human). The user 132 is not part ofthe network environment 100, but is associated with the device 130 andmay be a user of the device 130. For example, the device 130 may be adesktop computer, a vehicle computer, a tablet computer, a navigationaldevice, a portable media device, a smartphone, or a wearable device(e.g., a smart watch or smart glasses) belonging to the user 132, orwhich the user has access to. Likewise, the user 152 is not part of thenetwork environment 100, but is associated with the device 150. As anexample, the device 150 may be a desktop computer, a vehicle computer, atablet computer, a navigational device, a portable media device, asmartphone, or a wearable device (e.g., a smart watch or smart glasses)belonging to the user 152, or which the user has access to.

Any of the machines, databases, or devices shown in FIG. 1 may beimplemented in a general-purpose computer modified (e.g., configured orprogrammed) by software (e.g., one or more software modules) to be aspecial-purpose computer to perform one or more of the functionsdescribed herein for that machine, database, or device. For example, acomputer system able to implement any one or more of the methodologiesdescribed herein is discussed below with respect to FIG. 12. As usedherein, a “database” is a data storage resource, which operates inaccordance with storage module 230 and is accessed via access module210, to store data structured as a text file, a table, a spreadsheet, arelational database (e.g., an object-relational database), a triplestore, a hierarchical data store, or any suitable combination thereof.Moreover, any two or more of the machines, databases, or devicesillustrated in FIG. 1 may be combined into a single machine, and thefunctions described herein for any single machine, database, or devicemay be subdivided among multiple machines, databases, or devices.

The network 190 may be any network that enables communication between oramong machines, databases, and devices (e.g., the server machine 110 andthe device 130). Accordingly, the network 190 may be a wired network, awireless network (e.g., a mobile or cellular network), or any suitablecombination thereof. The network 190 may include one or more portionsthat constitute a private network, a public network (e.g., theInternet), or any suitable combination thereof. Accordingly, the network190 may include one or more portions that incorporate a local areanetwork (LAN), a wide area network (WAN), the Internet, a mobiletelephone network (e.g., a cellular network), a wired telephone network(e.g., a plain old telephone system (POTS) network), a wireless datanetwork (e.g., WiFi network or WiMax network), or any suitablecombination thereof. Any one or more portions of the network 190 maycommunicate information via a transmission medium. As used herein,“transmission medium” refers to any intangible (e.g., transitory) mediumthat is capable of communicating (e.g., transmitting) instructions forexecution by a machine (e.g., by one or more processors of such amachine), and includes digital or analog communication signals or otherintangible media to facilitate communication of such software.

FIG. 2 is a block diagram illustrating components of the server machine110, according to some example embodiments. The server machine 110 isshown as including access module 210, identification module 220, storagemodule 230, a communication module 240, policy module 250,recommendation module 260, and load balancer module 270, all configuredto communicate with each other (e.g., via a bus, shared memory, or aswitch). In one embodiment, load balancer module 270 may be part of aseparate server machine, and configured to communicate with modules210-260 (i.e., load balancer 270 is outside of server machine 110,implemented in a separate server machine or computing device). Any oneor more of the modules described herein may be implemented usinghardware (e.g., one or more processors of a machine) or a combination ofhardware and software. For example, any module described herein mayconfigure a processor (e.g., among one or more processors of a machine)to perform the operations described herein for that module. Inparticular, the load balancer module 270 performs the data flowdescribed with respect to the flowchart of FIG. 4. Moreover, any two ormore of these modules may be combined into a single module, and thefunctions described herein for a single module may be subdivided amongmultiple modules. Furthermore, according to various example embodiments,modules described herein as being implemented within a single machine,database, or device may be distributed across multiple machines,databases, or devices.

FIG. 3 is a block diagram illustrating components of a device suitablefor a workload balancer, according to some example embodiments. As seenin FIG. 3, a system 300, which may be the network-based system 105 ofFIG. 1, comprises a number of computer nodes 340, 350, 360, . . . , 370each comprising a respective computer machine, and each respectivelyrepresenting computer nodes which may be referred to as Node 1, Node 2,Node 3, . . . , Node N of FIG. 3. Associated with each node may be aload balancer agent such as 340-1, 350-1, 360-1, . . . , 370-1 of nodes340, 350, 360, . . . , 370. Load balancer 330, which in some embodimentsis software such as load balancer module 270 of FIG. 2, interfaces witheach node, in one embodiment, agents 340-1, 350-1, 360-1, . . . , 370-1in order to communicate with each node via communication module 240 ofFIG. 2. As discussed briefly above, when a new version of an applicationis deployed, the management system 300 brings the new version of theapplication up and then starts a load balancer agent such as agents340-1, 350-1, 360-1, . . . , 370-1 of nodes 340, 350, 360, . . . , 370,respectfully. The system 300 also provides, via identification module220, that the node trying to register is valid and part of a pool thatis authorized to receive work by, inter alia, checking node address,machine configuration, and application version numbers. Identificationmodule 220 communicates with policy module 250 that addresses securityrisks by implementing additional authentication. The agent thencommunicates via communication module 240 of FIG. 2 with the loadbalancer 330 for registering itself as ready to receive traffic. Asmentioned, the load balancer 330 may operate with or without agents.Operation of the load balancer 330 is described in additional detailbelow. With continued reference to FIG. 3, user 310 communicates withsystem 300 over the network 320 (which may the same as network 190 ofFIG. 1). When a user communicates with system 300, policy module 250provides security by such actions as verifying that a requestedcertificate is for a specific user and for a specific purpose, and itcan enforce whether to deploy a user certificate or computercertificate. The load balancer 330 interfaces with network 320 via anetwork interface of system 300.

FIG. 4 is a flowchart illustrating data flows within the networkenvironment suitable for load balancing, according to some exampleembodiments. The method of the flowchart of FIG. 4 begins with anordered list of computing nodes such as nodes 340, 350, 360, . . . , 370of FIG. 3 that are configured for system processing in operation 400. Asdiscussed above, the capacity of a given node may be determined by themaximum units of work that a node can process to be within therequirements of the SLA of the given node. Operations in the method 400may be performed using modules described above with respect to FIG. 2 asmore fully discussed below.

As shown in FIG. 4, the method 400 includes operations 400, 410, 420,430, 440, 450, 460, and 470. As a service request arrives to the loadbalancer module 270 of FIG. 2, via communication module 240 of FIG. 2,from a user 310, load balancer module 270 of FIG. 2 provides the servicerequest to the first node in the ordered list for processing atoperation 410 of FIG. 4. The load balancer module 270 communicates withthe nodes 340, 350, 360, . . . , 370 by communication buses via anetwork, such as network 190, via access module 210 of FIG. 2, andreceives feedback of service and resource availability from the nodethat is active at operation 420.

Based on the feedback, certain decisions may be made. As seen atoperation 430 a determination is made, by load balancer module 270interacting with access module 210 to address the active node, as towhether the active node both meets the SLA requirement and has anavailable processing slot (e.g., whether the number of connections tothe active node is less than the maximum number of connections for thenode). If the answer is YES, (i.e., the YES decision is taken), then theload balancer module 270 sends the next service request to the currentlyactive node at operation 440 via communication module 220 of FIG. 2.Additionally, the maximum number of connections for the node may beincremented by one, to allow another request to be routed to the activenode at a later time. If the NO decision is taken at operation 430, thena test is performed at operation 450 by load balancer module 270 of FIG.2 to determine whether the average service time of the active nodeexceeds an SLA threshold or whether busy threads of the active nodeexceed a maximum amount. The maximum amount may be determined by thesystem designer in accordance with the needs of the particular system.In one embodiment, if the NO decision is taken at operation 430, thenthe maximum amount of connections for the node may be decremented byone, such that future requests are not routed to that node. In oneembodiment, if a No decision is taken at operation 430, a degraded modeof operation may be utilized, if the current node is the last node in anordered list to be checked. Degraded mode is explained further herein.

If a NO decision is taken at operation 450, the system continues back tooperation 420 to receive feedback from the active node for a number oftimes for either the YES decision to be taken at operation 450 or theYES decision to be taken at test operation 430, described below. Thesystem designer may set a time-out period whereby if either the YESdecision at operation 430 or the YES decision at operation 450 is notreached during the time-out period, load balance module 270 issues analert indicting an abnormal condition is generated.

If the YES decision is taken at step 430, this indicates that the activenode is operating with the desired SLA requirement and does have anavailable processing slot. Consequently as at operation 440, the loadbalancer module 270 sends the next service request to the active node.

If the YES decision is taken at test operation 450, the active node isnot in condition to receive another service request and another nodeshould be initiated. At test operation 460, a determination is made byload balancer module 270 as to whether the active node is the last nodein the ordered list. If the NO decision is taken, then load balancermodule 270 sends the next service request to the next node in theordered list in operation 470. If the YES decision is taken, that meansthat all nodes in the ordered list are operating at maximum capacity,and a new node should be brought online and added to the ordered list inoperation 480.

In one embodiment, method 400 may not include a determination asdescribed above with respect to operation 450. In such an embodiment,after a NO decision is taken at operation 430, the method proceedsdirectly to operation 460. Such a method 400 is depicted in FIG. 4A.

One or more of operations 400-450 may be performed as part (e.g., aprecursor task, a subroutine, or a portion) of operation 460.

In one embodiment, the maximum number of connections for a node may beincremented, decremented, or kept the same, based on the deviation ofthe response time from the SLA, after a transaction is processed orcompleted. Thus, in one embodiment, after the completion of atransaction, information including the maximum number of connections,current number of connections, and processor utilization over a certaintime period may be transmitted to the load balancer. The load balancer,or a load balancer agent or daemon for each node (which may be executingon individual nodes), may use that information to determine whether tochange the maximum number of connections value for the node.

For example, in one embodiment, the load balancer agent or daemon forthe node may determine whether the deviation of the response time fromthe SLA exceeds a threshold to take early action, and may decrease themaximum number of connections for that node, which information is thentransmitted to the load balancer. Additionally, the load balancer agentor daemon for the node may determine whether the deviation of theresponse time from the SLA is less than the inverse of the threshold totake early action, and may increase the maximum number of connectionsfor that node, which information is then transmitted to the loadbalancer. An early action threshold may be used when the number ofconnections to the node does not equal the maximum number of connectionsto the node. In one embodiment, the threshold is the product of the SLAmultiplied by a fraction particular to the application executed by thenodes. These operations performed by the load balancer agent or daemonmay determine a score for the transaction, which ultimately results inthe adjustment to the maximum number of connections variable used by theload balancer.

Similarly, the load balancer agent or daemon for the node may determinethat the accumulated deviation of the response time from the SLA exceedsa threshold to take regular action, and may decrease the maximum numberof connections for that node, which information is then transmitted tothe load balancer. Additionally, the load balancer module or daemon forthe node may determine whether the accumulated deviation of the responsetime from the SLA is less than the inverse of the threshold to takeregular action, and may increase the maximum number of connections forthat node, which information is then transmitted to the load balancer. Aregular action threshold may be used when the number of currentconnections to the node equals the maximum number of connections to thenode. As above, these operations performed by the load balancer agent ordaemon may determine a score for the transaction, which ultimatelyresults in the adjustment to the maximum number of connections variableused by the load balancer.

In one embodiment, certain decisions made as part of operations ofmethod 400 may be modified to allow nodes to take burst traffic. Forexample, in some implementations of method 400, burst traffic may causethe load balancer to go into a round robin mode. Round robin mode willprevent starting a new node for temporary traffic bursts even if currentnodes have not met the SLA. Accordingly, in one embodiment, a lowwatermark buffer equal to half of the number of current connectionsbelow the amount of maximum connections, below which the maximumconnections amount will not be reduced may be provided to accommodatebursts after prolonged low traffic periods. Similarly, a high watermarkbuffer equal to half the number of current connections above the amountof maximum connections, above which the maximum connections amount willnot be increased, may be provided to prevent a misbehaving node or nodeencountering error conditions because of faulty software or otheroperating conditions which affect all transactions. Thus, in such anembodiment, an operating range for the maximum number of connections maybe provided, in which the maximum number of connections may varydepending on the number of current connections, so that bursts can behandled.

In one embodiment, the load balancer may be configured to deal withmultiple types of service requests, which, in one example, arecharacterized by a service request URL. In such an embodiment, multipleaccumulators may be provided, one for each set of service request URLs.Each accumulator is updated after a transaction response occurs with anormalization of the difference between the response time for thetransaction and the service level agreement for that type oftransaction. The accumulator value calculated after each transactionresponse may be used as feedback to the load balancer, by using theaccumulator value to update the maximum number of connections variablefor a particular node. Thus, as a service request arrives to the loadbalancer, the load balancer may pool a number of related transactionstogether, and distribute those pooled transactions to one or more nodes.Pooling transactions in this manner may take advantage of caching builtup by related transactions.

In one embodiment, transactions that may be grouped include thosetransactions with a higher frequency, but lower response timerequirements. Similarly, transactions that may be grouped are lower infrequency but higher in response times. This may ensure the least amountof unused resources when a given node processes elongated transactionsof a certain type in a group of transactions.

In one embodiment, grouping transactions (or URLs) may includecalculating the product of the service level agreement, multiplied bythe frequency of execution for each transaction. From these calculatedproducts, the natural separation of the products may be identified toidentify groups. Transactions represented by the calculated products maybe clustered into those groups. For each group of transactions or URLs,an accumulator is allocated to accumulate the gain and loss of servicetimes, with respect to the service level agreement.

The sum of such gain or loss of service time may represent a total workamount that can be processed at a given time by processing unitsavailable in a node. In one embodiment, the number of processing unitsplus the common queue length that can be accommodated for the expectedservice time of the given transactions is the maximum number of activeconnections that may be allowed on the node. Connections may then beproportionately divided into the number of accumulators formed by theidentified groups of transactions.

Thus, in one embodiment, incoming transactions may be routed to anappropriate load balancer module for the transaction type of theincoming transaction. The load balancer module includes an accumulator,which may be recalculated upon the completion of each transactionleaving the node. That is, as any transaction leaves a node, oneaccumulator value may be calculated. During the computation of the gainor loss in transaction service time, the transaction service time may becompared to the service level agreement of the transaction's type. Theoutput of any accumulator changing the maximum number of connections maybe transmitted to the load balancer.

In one embodiment, a combination of the above techniques may be utilizedfor the load balancer. Thus, a single load balancer process may beemployed which operates in connection with a front end process. Thefront end process may determine to which group a given incoming requestbelongs, and thus which group load balancer module should process theincoming request. Each group load balancer module may maintain its ownset of accumulators, with one accumulator per node. The individual groupload balancer modules do not interact with other such modules. For eachgroup, the service level agreement or response time expectation may bedifferent from that of other groups, and thus the configurationsmaintained by the group load balancer modules are different. Each groupload balancer module may operate as a load balancer according to theabove description of handling burst traffic, that is, a load balancerthat recalculates the maximum number of connections with a buffer. Allgroup load balancer modules may connect to the same set of nodesexecuting the same application.

FIG. 5 is a block diagram illustrating components of a device suitablefor a workload balancer with a plurality of group load balancer modules,according to some example embodiments. FIG. 5 is similar in somerespects to the block diagram of FIG. 3. As seen in FIG. 5, a system500, which may be the network-based system 105 of FIG. 1, comprises anumber of computer nodes 540, 550, 560, . . . , 570 each comprising arespective computer machine, and each respectively representing computernodes which may be referred to as Node 1, Node 2, Node 3, . . . , Node Nof FIG. 5. Associated with each node may be a load balancer agent suchas 540-1, 550-1, 560-1, . . . , 570-1 of nodes 540, 550, 560, . . . ,570. In some implementation, the functionality of the agents 540-1,550-1, 560-1, . . . , 570-1 is included load balancer itself, and eachnode is not associated with its own load balancer agent. Load balancer530, which in some embodiments is software such as load balancer module270 of FIG. 2, interfaces with each node, in one embodiment, agents540-1, 550-1, 560-1, . . . , 570-1 in order to communicate with eachnode via communication module 240 of FIG. 2. Load balancer 530 furtherincludes one or more group load balancer modules 535-1, 535-2, 535-3.Three group load balancer modules are depicted in FIG. 5, though more orfewer may be present. Group load balancer modules interface with eachnode 540, 550, 560, . . . , 570, and in some embodiments, agents 540-1,550-1, 560-1, . . . , 570-1 in order to communicate with each node viacommunication module 240 of FIG. 2. Each group load balancer module 535may maintain a set of accumulator values, corresponding to the nodes itis communicating with, for transactions processed by that group.Operation of the load balancer 530 and group load balancer modules535-1, 535-2, 535-3 is described in additional detail below. Withcontinued reference to FIG. 5, user 510 communicates with system 500over the network 520 (which may the same as network 190 of FIG. 1). Whena user communicates with system 500, policy module 250 provides securityby such actions as verifying that a requested certificate is for aspecific user and for a specific purpose, and it can enforce whether todeploy a user certificate or computer certificate. The load balancer 530interfaces with network 520 via a network interface of system 500.

In one embodiment, commands are grouped by identifying similar commandsbased on the distribution of their service level agreement needs orresponse times. This may be performed, in one embodiment, by collectingdata from logs of current or previous transactions. Because transactionvolume may vary based on the time of day, commands may be grouped on aperiodic basis. For example, commands may be grouped for every 15minutes during a day, or other time period. In one embodiment, a trafficpattern that replicates periodically may be used to perform trafficanalysis. For example, traffic patterns may replicate on a weekly basis.Accordingly, grouping and traffic analysis for a 15-minute period duringa particular week may be used to determine command groups for the same15-minute period the next week.

In one embodiment, commands are grouped based on the classification ofcommands using clustering analysis. In one embodiment, k-meansclustering is used, although other clustering techniques are possible aswell. Clustering may be used, in one embodiment, to limit the number oftypes or classes of transactions to a number that can be determined,depending on available resources. That is, commands can be clusteredinto many or few clusters, and an appropriate number of group loadbalancer modules may be provided. In one embodiment, the response timemedian for the given command, and the 95^(th) percentile response timefor the command may be used as the dimensions for the clusteringanalysis. In one embodiment, the 95^(th) percentile response time forthe command may be approximately two times the average response timevalue. Thus, in one example embodiment, a starting point for centroidsof the clusters may be the response time median and two times theaverage response time.

To determine the number of groups to classify commands into (i.e., thevalue of “k” in a k-means analysis), peaks in response time distributiongraph may be identified. Thus, the number of peaks in a response timedistribution graph may be used as the “k” value to classify the commandsinto. In some instances, response time peaks may be the result of valuesfrom multiple commands. In such instances, when it is determined thatsuch a peak is not the result of a high number of values from a singlecommand, the number of groups may be reduced by one.

In one embodiment, the centroids are the points around which a responsetime distribution occurs. Thus, a k-means algorithm may use theidentified centroids as the “k” number of groups. Each command, based onits response time, is categorized or assigned into one of the “k” groupsbased upon which group's response time the command's response time isclosest to. As explained above, the number of groups and categorizationof the commands into those groups may be recalculated for each timeperiod (e.g., 15 minute time period) per week or other length of time.

Thus, as described above, a single load balancer 530 may be employedwhich operates in connection with a front end process. The front endprocess may determine to which group a given incoming request belongs,and thus which group load balancer module 535-1, 535-2, 535-3, shouldprocess the incoming request. In one embodiment, the number of loadbalancer modules is the number of “k” clusters. Thus, in the example ofFIG. 5, “k” may be determined to be three. Requests may be sent to thefirst node that permits an additional connection for the group therequest belongs to. Accordingly, nodes which are started early shouldreceive the most traffic.

For normal routing, when a request is received, the request is initiallyassigned to the first active node. A determination is made as to whetherthe number of connections for the first active node is less than themaximum number of connections for that node. If that determinationresults in a yes, the request is routed to that node. If thatdetermination results in a no, the next active node should be checked.If all active nodes have been checked and the request cannot be routed,then the routing process may proceed to a degraded state, describedbelow. Otherwise, the number of connections for the next active node isevaluated against the maximum number of connections for that node, untilthe request is routed, or until the routing process proceeds to adegraded state. A method of routing requests as described is shown withmore detail in FIG. 6, which, in certain operations, is similar to themethod outlined in FIG. 4.

Method 600 of FIG. 6 begins at operation 610, where a service request isreceived by load balancer 530, for example, from a user 510 over network520. At operation 620, the load balancer 530 may determine anappropriate group and thus an appropriate group load balancer module535-1, 535-2, 535-3, for the service request, based, for example, on theexpected response time or service level agreement for the particulartype of request. Thus, as described above, the load balancer 530 maydetermine that a group load balancer module 535-1 for group 1 isappropriate for the received request.

At operation 625, a numeral identifier value for a node to be checked isset to 1. The numeral identifier value identifies the node (which may bethe first node in an ordered list) which is checked to determine whetherthe node has availability to process the request. At operation 630, agroup load balancer module (e.g., group load balancer module 535-1)proceeds to attempt to route the request by starting at the firstcomputing node, for example, the first computing node in an orderedlist. In one embodiment, the group software load balancer module 535performs a comparison against a numeral identifier variable for the nodeto be checked (e.g., for the first node a numeral identifier variable of“1”) against a maximum number of nodes variable (e.g., “5”, if there arefive available nodes). If the variable for the first node is less thanthe maximum number of nodes variable, method 600 proceeds to operation640.

At operation 640, the number of connections for the first node or thenode to be checked (denoted as “Conn,” or written as functionalnotation, “CONN(NODE_NUM)”) is evaluated against the maximum number ofconnections for the first node (denoted as “CMax”, or written asfunctional notation, “CMAX(NODE_NUM)”) to determine whether the firstnode has an available slot to process the request. If the number ofconnections for the first node is less than the maximum number ofconnections for the first node, method 600 proceeds to operation 650,and the request is routed to the first node. The first node may thenprocess the request. If the number of connections for the first node isequal to or greater than the maximum number of connections for the firstnode, method 600 proceeds to operation 660, which increments the numeralidentifier variable for the node to be checked (e.g., incrementing thenumeral identifier variable for the node to be checked to “2”). Method600 then returns back to operation 630, and the operations repeat untilthe request is routed.

If, at operation 630, the numeral identifier variable for the node to bechecked exceeds the maximum number of nodes variable (which may occur ifno individual node has an available connection), method 600 proceeds tooperation 670, the degraded state of operation, which is described inmore detail in FIG. 7.

If a request cannot be routed according to the operations of method 600(for example, if additional capacity or nodes are not immediately added,or because temporary traffic bursts do not warrant new capacity), theload balancer may enter a degraded state, in which performance on agroup of commands is degraded from the target SLA. In one embodiment,the performance on the group of commands is gradually degraded untilperformance can be returned to a target service level agreement. In oneembodiment, performance is reverted back to a target service levelagreement or response time expectation as soon as possible. The degradedstate is entered when no node can process the current request accordingto the target SLA. The operation of the degraded state is described withreference to method 700 of FIG. 7, which, in one embodiment, isperformed by a group load balancer module 535.

Method 700 begins at operation 710, where a degraded state variable isset to an initial value of one. At operation 715, a numeral identifiervalue for a node to be checked is set to 1. The numeral identifier valueidentifies the node which is checked to determine whether the node hasavailability to process the request. Method 700 then proceeds tooperation 720, where a degradation factor variable is calculated to bethe difference between the degraded state variable minus one, times afactor of 0.1 (representing a 10% degradation of the target servicelevel agreement or response time expectation). Method 700 then proceedsto operation 730, where, similar to operation 630, a group load balancermodule 535 may attempt to route the request by starting at the firstcomputing node, and performing a comparison against the numeralidentifier variable for the node to be checked against the maximumnumber of nodes variable (e.g., “5”, if there are five available nodes).If the variable for the node to be checked is less than the maximumnumber of nodes variable, method 700 proceeds to operation 740. Atoperation 740, similar to operation 640, the number of activeconnections for the first node (or the current node to be checked) isevaluated to determine whether the request may be routed to the firstnode. In operation 740, however, the number of active connections to thefirst node is evaluated against a degraded state maximum number ofconnections for the first node (denoted as “Cmax2”), i.e. Cmaxmultiplied by the degradation factor variable, rounded up to the nextinteger. If the comparison at operation 740 results in a YES value,method 700 proceeds to operation 750, and the request is routed to thefirst node or the node being checked. In one embodiment, the degradationfactor variable is reset, as the next request may be serviced within anormal (i.e., non-degraded) service level agreement, as nodes finishprocessing earlier requests. If the comparison at operation 740 resultsin a NO value, method 700 proceeds to operation 760, where the numeralidentifier for the node to be checked is incremented so as to check thenext available node in the list, and method 700 returns to operation730.

If, at operation 730, the numeral identifier variable for the node to bechecked exceeds the maximum number of nodes variable (which may occur ifno individual node has an available connection to service the requestwith one level of degradation), method 700 proceeds to operation 770. Atoperation 770, the degraded state variable is incremented by one. Method700 then proceeds again to operation 720, where the degradation factorvariable is recalculated, and operations 730, 740, and 760 repeat, untilthe request is successfully routed at operation 750.

After a request is routed, and after the request is processed orresponded to, the maximum number of connections for the node may beupdated to the maximum number of connections in a non-degraded state(i.e., the CMax value). A flowchart of a method 800 for updating themaximum number of connections for a node is depicted in FIG. 8. Method800 may be performed by, in one embodiment, an agent 540-1, 550-1, 560-1. . . 570-1, or by a group load balancer module 535-1, 535-2, 535-3. Atoperation 810 of method 800, the response time for the request isdetermined (denoted as “Resp”). An accumulator variable or value for thenode is then increased by the difference between the response time forthe request (RESP) and the median service level agreement for therequest type at operation 820. In some implementations, operation 820may result in a decrease in the accumulator value if the response timeof the request (RESP) is less than the service level agreement for thatrequest type. At operation 830, a determination is made, based on threeseparate factors. If the result of any of the factors is a positivedetermination, method 800 proceeds to operation 840. First, at operation830, if the accumulator value is greater than the standard deviation ofthe response time (e.g., the difference between the response time andthe mean response time) multiplied by the square root of the number oftransactions which resulted in the accumulator value, method 800proceeds to operation 840. Second, at operation 830, if the accumulatorvalue is less than the opposite of the standard deviation multiplied bythe square root of the number of transactions which resulted in theaccumulator value, method 800 proceeds to operation 840. Third, if thecurrent request number (N) is less than the number of requests beingaccumulated, method 800 proceeds to operation 840.

At operation 840, a determination is made as to whether the accumulatorvalue is greater than the standard deviation of the response timemultiplied by the square root of the number of transactions whichresulted in the accumulator value. If so, method 800 proceeds tooperation 850. At operation 850, a determination is made as to whetherthe number of connections to the node (“CONN”) exceeds the maximumnumber of connections for the node (“CMAX”). If operation 850 results ina NO, method 800 proceeds to operation 860, where the maximum number ofconnections is set to the maximum of: the maximum number of connectionsminus one, and one. Method 800 then proceeds to operation 870, where theaccumulator value is reset for accumulating new response timedifferences. In one embodiment, the accumulator value is reset, alongwith a count of the number of transactions which resulted in theaccumulator value (“N”), and a count of the number of transactions whichresulted in the accumulator value which occurred in the degraded state(“N2”). If operation 840 results in a YES, method 800 proceeds directlyto operation 870, where the number of transactions is reset, and onlythe accumulator value is reset.

At operation 840, if the accumulator value is not greater than thestandard deviation of the response time multiplied by the square root ofthe number of transactions which resulted in the accumulator value,method 800 proceeds to operation 880. At operation 880, a determinationis made based on whether the accumulator value is less than the oppositeof the standard deviation multiplied by the square root of the number oftransactions which resulted in the accumulator value. If operation 880results in a YES, method 800 proceeds to operation 890, where themaximum number of connections is set to the minimum of the currentmaximum number of connections plus one, or the current number ofconnections plus five. If operation 880 results in a NO, then the thirdcondition at operation 830 exists; specifically, the maximum number ofconnections to the node does not exceed the number of connections to thenode, and therefore method 800 proceeds to operation 870, where theaccumulator value is reset.

If, at operation 830, all three factors result in a NO, method 800proceeds to operation 895. At operation 895, the number of transactionsis increased by one, and the maximum number of connections for the nodeis not updated. Method 800 may then take place again after anotherrequest is routed to a node and processed by that node.

As described above, the operations of method 800 occur after a requestis routed and processed by a node, to provide feedback in the form ofthe response time, which is then incorporated into the maximum number ofconnections for the node. In one embodiment, the degraded state maximumnumber of connections for a node (i.e., the CMax2 value) is also updatedafter a request is routed and processed by the node. FIG. 9 depicts aflowchart of a method 900 for updating the degraded state maximum numberof connections for a node, which is similar in some respects to themethod 800 of FIG. 8. Method 900 may be performed by, in one embodiment,an agent 540-1, 550-1, 560-1 . . . 570-1, or by a group load balancermodule 535-1, 535-2, 535-3.

At operation 910 of method 900, the response time for the request isdetermined. An accumulator variable for the node is then increased bythe difference between the response time for the request and the medianservice level agreement for the request type, multiplied by 1.1 (for thedegradation factor), at operation 920. At operation 930, a determinationis made, based on three separate factors. If the result of any of thefactors is a positive determination, method 900 proceeds to operation940. First, at operation 930, if the accumulator value is greater thanthe standard deviation of the response time (e.g., the differencebetween the response time and the mean response time) multiplied by thesquare root of the number of transactions in the degraded state (denotedas “N2”) which resulted in the accumulator value, method 900 proceeds tooperation 940. Second, at operation 930, if the accumulator value isless than the opposite of the standard deviation multiplied by thesquare root of the number of transactions in the degraded state whichresulted in the accumulator value, method 900 proceeds to operation 940.Third, if the degraded state maximum number of connections to the nodeis greater than the number of connections to the node, method 900proceeds to operation 940.

At operation 940, a determination is made as to whether the accumulatorvalue is greater than the standard deviation of the response timemultiplied by the square root of the number of transactions in thedegraded state which resulted in the accumulator value. If so, method900 proceeds to operation 950. At operation 950, a determination is madeas to whether the number of connections to the node (denoted as “CONN”)exceeds the degraded state maximum number of connections for the node(denoted as “CMAX2”). If operation 950 results in a NO, method 900proceeds to operation 960, where the degraded state maximum number ofconnections is set to the maximum of: the degraded state maximum numberof connections minus one, and one. Method 900 then proceeds to operation970, where the accumulator value is reset. If operation 950 results in aYES, method 900 proceeds to operation 970, where the number oftransactions is reset, and the accumulator value is reset.

At operation 940, if the accumulator value is not greater than thestandard deviation of the response time multiplied by the square root ofthe number of transactions in the degraded state which resulted in theaccumulator value, method 900 proceeds to operation 980. At operation980, a determination is made based on whether the accumulator value isless than the opposite of the standard deviation multiplied by thesquare root of the number of transactions in the degraded state whichresulted in the accumulator value. If operation 980 results in a YES,the maximum number of connections is set to the minimum of the currentdegraded state maximum number of connections plus one, or the currentnumber of connections plus five. If operation 980 results in a NO, thenthe third condition at operation 930 exists; specifically, the number ofconnections to the node exceeds the maximum number of connections to thenode, and therefore method 900 proceeds to operation 970, where theaccumulator value is reset.

If, at operation 930, all three factors result in a NO, method 900proceeds to operation 995. At operation 995, the number of transactionsis increased by one, and the maximum number of connections for the nodeis not updated. Method 990 may then take place again after anotherrequest is routed to a node and processed by that node.

According to various example embodiments, one or more of themethodologies described herein may facilitate load balancing. Moreover,one or more of the methodologies described herein may facilitateregistering computing nodes or applications for operation. Hence, one ormore the methodologies described herein may facilitate load balancing,as well as registering computing nodes or applications for operation.

When these effects are considered in aggregate, one or more of themethodologies described herein may obviate a need for certain efforts orresources that otherwise would be involved in load balancing. Effortsexpended by a user in load balancing may be reduced or made moreefficient by one or more of the methodologies described herein.Computing resources used by one or more machines, databases, or devices(e.g., within the network environment 100) may similarly be reduced.Examples of such computing resources include processor cycles, networktraffic, memory usage, data storage capacity, power consumption, andcooling capacity.

FIG. 10A is a graph illustrating how many transactions are beingprocessed by each active machine, or node, of an ordered list of nodes,according to some embodiments, at a given time, where the x-axisrepresents time. FIG. 10B is a graph illustrating a utilization level ofthe active machines or nodes, of an ordered list of nodes, according tosome embodiments. FIG. 10C illustrates average response time of thetransactions processed by a node. Each of the graphs of FIGS. 10A-10Crepresents a one minute perfmon, or monitor of performance. Each pointon the graphs represents the corresponding value of the attributeaveraged over one minute. FIG. 11 is a graph illustrating steadilyincreasing, and then decreasing, transactions arriving for an orderedlist of nodes, according to some embodiments. As can be seen in FIG. 11,transactions 1100 coming into this set of nodes will be steadilyincreasing and then begin decreasing at about 20 h53. The time stamps inFIG. 11 are in PST while those in the graphs of FIGS. 10A-10C are inMST.

As can be seen in FIG. 10A, a first machine 1010A takes most of thetransactions as illustrated by the rising plot of 1010A in FIG. 10A,with corresponding rise in CPU utilization. This corresponding rise inCPU utilization is see as the rising plot 1010B of FIG. 5B, until alittle after 21 h30 when the response time begins to violate the givenSLA, of 1 sec in the current example embodiment. At that point, the loadbalancer 330 of FIG. 3, which operates through load balancer module 270of FIG. 2, starts giving the newly incoming transactions to the secondnode 1020A of FIG. 10A and the first two nodes 1010A and 1020A get intodynamic equilibrium. At approximately 21 h40, as the second node 1020Aprocesses as many transactions as it can within the given SLA and startsviolating the SLA for anything more, third node 1030A is accessed toprocess any additional transactions. All three nodes 1010A, 1020A, and1030A are sharing the incoming transactions equally at approximately 21h53 (20 h53 PST) when incoming load 600 starts falling as seen in FIG.11. At that point, the last node brought in, 1030A, is gradually let go,followed by the second node 1020B, followed by the first node 1010A. Inother words, the nodes are brought in as and when needed and let go asand when not needed. It can also be seen that all three machines puttogether do not have enough computing resources to complete the incomingtransactions within SLA around 21 h55, and the system lets the SLAsuffer briefly as there was no new node brought in at that time. Hadthere been a fourth node available, this system would have startedgiving the incoming transactions to that one to keep the SLA, and thatwould be first node to be let go when the incoming traffic slows downaround 20 h56 MST.

FIG. 12 is a block diagram illustrating components of a machine 1200,according to some example embodiments, able to read instructions 1224from a machine-readable medium 1222 (e.g., a non-transitorymachine-readable medium, a machine-readable storage medium, acomputer-readable storage medium, or any suitable combination thereof)and perform any one or more of the methodologies discussed herein, inwhole or in part. Specifically, FIG. 12 shows the machine 1200 in theexample form of a computer system (e.g., a computer) within which theinstructions 1224 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 1200 toperform any one or more of the methodologies discussed herein may beexecuted, in whole or in part.

In alternative embodiments, the machine 1200 operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 1200 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a distributed (e.g., peer-to-peer)network environment. The machine 1200 may be a server computer, a clientcomputer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a cellular telephone, a smartphone, a set-top box(STB), a personal digital assistant (PDA), a web appliance, a networkrouter, a network switch, a network bridge, or any machine capable ofexecuting the instructions 1224, sequentially or otherwise, that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executethe instructions 1224 to perform all or part of any one or more of themethodologies discussed herein.

The machine 1200 includes a processor 1202 (e.g., a central processingunit (CPU), a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), or any suitable combinationthereof), a main memory 1204, and a static memory 1206, which areconfigured to communicate with each other via a bus 1208. The processor1202 may contain microcircuits that are configurable, temporarily orpermanently, by some or all of the instructions 1224 such that theprocessor 1202 is configurable to perform any one or more of themethodologies described herein, in whole or in part. For example, a setof one or more microcircuits of the processor 1202 may be configurableto execute one or more modules (e.g., software modules) describedherein.

The machine 1200 may further include a graphics display 1210 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine1200 may also include an alphanumeric input device 1212 (e.g., akeyboard or keypad), a cursor control device 1214 (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, an eye trackingdevice, or other pointing instrument), a storage unit 1216, an audiogeneration device 1218 (e.g., a sound card, an amplifier, a speaker, aheadphone jack, or any suitable combination thereof), and a networkinterface device 1220.

The storage unit 1216 includes the machine-readable medium 1222 (e.g., atangible and non-transitory machine-readable storage medium) on whichare stored the instructions 1224 embodying any one or more of themethodologies or functions described herein. The instructions 1224 mayalso reside, completely or at least partially, within the main memory1204, within the processor 1202 (e.g., within the processor's cachememory), or both, before or during execution thereof by the machine1200. Accordingly, the main memory 1204 and the processor 1202 may beconsidered machine-readable media (e.g., tangible and non-transitorymachine-readable media). The instructions 1224 may be transmitted orreceived over the network 190 via the network interface device 1220. Forexample, the network interface device 1220 may communicate theinstructions 1224 using any one or more transfer protocols (e.g.,hypertext transfer protocol (HTTP)). The machine may function with theInternet Protocol (IP) as a communications protocol in an Internetprotocol suite for relaying datagrams across network boundaries. Therouting function of the IP enables internetworking via the Internet. TheInternet protocol suite has the task of delivering packets from thesource host to the destination host based on the IP addresses in thepacket headers. For this purpose, IP defines packet structures thatencapsulate the data to be delivered. It also defines addressing methodsthat are used to label the datagram with source and destinationinformation. The connection-oriented Transmission Control Protocol (TCP)may be used, often referred to as TCP/IP. The machine may operate withvarious versions of IP, including without limitation, Internet ProtocolVersion 4 (IPv4), Internet Protocol Version 6 (IPv6), and may be adaptedfor other and future protocols. The apparatus may function with variouslayers including an application layer, transport layer, Internet layerand link layer. Various transport layers may be used in addition to TCP.These transport layers may include User Datagram Protocol (UDP),Datagram Congestion Protocol (DCCP), Stream Control TransmissionProtocol (SCTP), Resource Reservation Protocol (RSVP), and others. Inoperation, the request for compute service is initiated at a clientmachine by a user selecting a button, or selectable icon, for making therequest at a user interface (UI) of the client machine. There is then amessage exchange between the server and the client machine, the messageexchange utilizing a network interface of the client machine and anetwork interface of the server.

In some example embodiments, the machine 1200 may be a portablecomputing device, such as a smart phone or tablet computer, and have oneor more additional input components 1230 (e.g., sensors or gauges).Examples of such input components 1230 include an image input component(e.g., one or more cameras), an audio input component (e.g., amicrophone), a direction input component (e.g., a compass), a locationinput component (e.g., a global positioning system (GPS) receiver), anorientation component (e.g., a gyroscope), a motion detection component(e.g., one or more accelerometers), an altitude detection component(e.g., an altimeter), and a gas detection component (e.g., a gassensor). Inputs harvested by any one or more of these input componentsmay be accessible and available for use by any of the modules describedherein.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1222 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions. The term “machine-readable medium” shall also be taken toinclude any medium, or combination of multiple media, that is capable ofstoring the instructions 1224 for execution by the machine 1200, suchthat the instructions 1224, when executed by one or more processors ofthe machine 1200 (e.g., processor 1202), cause the machine 1200 toperform any one or more of the methodologies described herein, in wholeor in part. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as cloud-based storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” shall accordingly be taken to include,but not be limited to, one or more tangible (e.g., non-transitory) datarepositories in the form of a solid-state memory, an optical medium, amagnetic medium, or any suitable combination thereof.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute softwaremodules (e.g., code stored or otherwise embodied on a machine-readablemedium or in a transmission medium), hardware modules, or any suitablecombination thereof. A “hardware module” is a tangible (e.g.,non-transitory) unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, and such a tangible entity may bephysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software(e.g., a software module) may accordingly configure one or moreprocessors, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, a processor being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented modules. As used herein,“processor-implemented module” refers to a hardware module in which thehardware includes one or more processors. Moreover, the one or moreprocessors may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)).

The performance of certain operations may be distributed among the oneor more processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, theone or more processors or processor-implemented modules may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theone or more processors or processor-implemented modules may bedistributed across a number of geographic locations.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a machine memory (e.g.,a computer memory). Such algorithms or symbolic representations areexamples of techniques used by those of ordinary skill in the dataprocessing arts to convey the substance of their work to others skilledin the art. As used herein, an “algorithm” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms and operations involve physical manipulation ofphysical quantities. Typically, but not necessarily, such quantities maytake the form of electrical, magnetic, or optical signals capable ofbeing stored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or any suitable combination thereof), registers, orother machine components that receive, store, transmit, or displayinformation. Furthermore, unless specifically stated otherwise, theterms “a” or “an” are herein used, as is common in patent documents, toinclude one or more than one instance. Finally, as used herein, theconjunction “or” refers to a non-exclusive “or,” unless specificallystated otherwise.

What is claimed is:
 1. A computer implemented method comprising:receiving, by one or more hardware processors, a sequence of requestsfor a computing service; determining, by the one or more hardwareprocessors, that a first computing node in an ordered list of computingnodes has a capacity to process a first request from the sequence ofrequests for the computing service based on determining that processingthe first request does not cause the first computing node to exceed amaximum capacity assigned to the first computing node; in response todetermining that the first computing node has the capacity to processthe first request, distributing, by the one or more hardware processors,the first request to the first computing node; receiving, by the one ormore hardware processors from the first computing node, feedbackinformation including a response time for processing the first request;modifying, by the one or more hardware processors, the maximum capacityassigned to the first computing node based on the feedback information;and distributing, by the one or more hardware processors, a secondrequest from the sequence of requests for the computing service to thefirst computing node based at least in part on the modified maximumcapacity assigned to the first computing node.
 2. The method of claim 1,further comprising distributing a third request of the sequence ofrequests for computing services to a second computing node in theordered list of computing nodes, based at least in part on adetermination that the first computing node does not have a capacity toprocess the third request.
 3. The method of claim 1, further comprising:determining that no computing node in the ordered list of computingnodes has capacity to process a third request from the sequence ofrequests for the computing service according to a first service levelrequirement; and switching to a degraded service mode by distributingthe third request to the first computing node in the ordered list ofcomputing nodes based at least in part on a determination that the firstcomputing node has a capacity to process the third request according toa second service level requirement.
 4. The method of claim 3, furthercomprising distributing a second fourth request of the sequence ofrequests for the computing services to a second computing node in theordered list of computing nodes based at least in part on adetermination that the first computing node does not have a capacity toprocess the second request according to the second service levelrequirement.
 5. The method of claim 3, wherein the degraded service modeis a first degraded service mode, the method further comprising:determining that no computing node in the ordered list of computingnodes has capacity to process a fourth request of the sequence ofrequests for the computing service according to the second service levelrequirement; and switching to a second degraded service mode bydistributing the fourth request to the first computing node in theordered list of computing nodes based at least in part on adetermination that the first computing node has a capacity to processthe fourth request according to a third service level requirement. 6.The method of claim 1, further comprising determining, from the sequenceof requests for computing service, a plurality of groups of requestsbased on a cluster-based analysis of the sequence of requests for thecomputing service, wherein the first and second requests belong to afirst group in the plurality of groups.
 7. The method of claim 1,wherein modifying the maximum capacity for the first computing nodeincludes determining whether aggregated response times for the firstcomputing node satisfy a predetermined threshold based on a firstservice level requirement.
 8. A non-transitory machine readable mediumhaving stored thereon machine-readable instructions executable to causea machine to perform operations comprising: receiving a sequence ofrequests for a computing service; determining that a first computingnode in an ordered list of computing nodes has a capacity to process afirst request from the sequence of requests for the computing servicebased on determining that processing the first request does not causethe first computing node to exceed a maximum capacity assigned to thefirst computing node; in response to determining that the firstcomputing node has the capacity to process the first request,distributing the request for the computing service to the firstcomputing node; receiving, from the first computing node, feedbackinformation including a response time for processing the first request;modifying the maximum capacity assigned to the first computing nodebased on the feedback information; and distributing a second requestfrom the sequence of requests for the computing service to the firstcomputing node based at least in part on the modified maximum capacityassigned to the first computing node.
 9. The non-transitory machinereadable medium of claim 8, wherein the operations further comprisedistributing a third request of the sequence of requests for thecomputing services to a second computing node in the ordered list ofcomputing nodes based at least in part on a determination that the firstcomputing node does not have a capacity to process the third request.10. The non-transitory machine readable medium of claim 8, wherein theoperations further comprise: determining that no computing node in theordered list of computing nodes has capacity to process a third requestof the sequence of requests for the computing service according to afirst service level requirement; and switching to a degraded servicemode by distributing the third request to the first computing node inthe ordered list of computing nodes based at least in part on adetermination that the first computing node has a capacity to processthe third request according to a second service level requirement. 11.The non-transitory machine readable medium of claim 10, wherein theoperations further comprise distributing a fourth request of thesequence of requests for the computing services to a second computingnode in the ordered list of computing nodes based at least in part on adetermination that the first computing node does not have a capacity toprocess the fourth request according to the second service levelrequirement.
 12. The non-transitory machine readable medium of claim 10,wherein the degraded service mode is a first degraded service mode, andwherein the operations further comprise: determining that no computingnode in the ordered list of computing nodes has capacity to process afourth request of the sequence of requests for the computing serviceaccording to the second service level requirement; and switching to asecond degraded service mode by distributing the fourth request to thefirst computing node in the ordered list of computing nodes based atleast in part on a determination that the first computing node has acapacity to process the fourth request according to a third servicelevel requirement.
 13. The non-transitory machine readable medium ofclaim 8, wherein the operations further comprise determining, from thesequence of requests for the computing service, a plurality of groups ofrequests based on a cluster-based analysis of requests for computingservice, wherein the first and second requests belong to a first groupin the plurality of groups.
 14. The non-transitory machine readablemedium of claim 8, wherein modifying the maximum capacity for the firstcomputing node includes determining whether aggregated response timesfor the first computing node satisfy a predetermined threshold based ona first service level requirement.
 15. A system comprising: anon-transitory memory; and one or more hardware processors coupled withthe non-transitory memory and configured to read instructions from thenon-transitory memory to cause the system to perform operationscomprising: receiving a sequence of requests for a computing service;determining that a first computing node in an ordered list of computingnodes has a capacity to process a first request from the sequence ofrequests for the computing service based on determining that processingthe first request does not cause the first computing node to exceed amaximum capacity assigned to the first computing node; in response todetermining that the first computing node has the capacity to processthe first request, distributing the first request to the first computingnode; receiving, from the first computing node, feedback informationincluding a response time for processing the first request; modifyingthe maximum capacity assigned to the first computing node based on thefeedback information; and distributing a second request from thesequence of requests for the computing service to the first computingnode based at least in part on the modified maximum capacity assigned tothe first computing node.
 16. The system of claim 15, wherein theoperations further comprise distributing a third request of the sequenceof requests for the computing services to a second computing node in theordered list of computing nodes based at least in part on adetermination that the first computing node does not have a capacity toprocess the third request.
 17. The system of claim 15, wherein theoperations further comprise: determining that no computing node in theordered list of computing nodes has capacity to process a third requestfrom the sequence of requests for the computing service according to afirst service level requirement; and switching to a degraded servicemode by distributing the third request to the first computing node inthe ordered list of computing nodes based at least in part on adetermination that the first computing node has a capacity to processthe third request according to a second service level requirement. 18.The system of claim 17, wherein the operations further comprisedistributing a fourth request of the sequence of requests for thecomputing services to a second computing node in the ordered list ofcomputing nodes based at least in part on a determination that the firstcomputing node does not have a capacity to process the fourth requestaccording to the second service level requirement.
 19. The system ofclaim 17, wherein the degraded service mode is a first degraded servicemode, and wherein the operations further comprise: determining that nocomputing node in the ordered list of computing nodes has capacity toprocess a fourth request of the sequence of requests for the computingservice according to the second service level requirement; and switchingto a second degraded service mode by distributing the fourth request tothe first computing node in the ordered list of computing nodes based atleast in part on a determination that the first computing node has acapacity to process the fourth request according to a third servicelevel requirement.
 20. The system of claim 15, wherein the operationsfurther comprise determining, from the sequence of requests for thecomputing service, a plurality of groups of requests based on acluster-based analysis of the sequence of requests for computingservice, and wherein the first and second requests belong to a firstgroup in the plurality of groups.